Overview

Brought to you by YData

Dataset statistics

Number of variables15
Number of observations16942236
Missing cells1037769
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.5 GiB
Average record size in memory283.7 B

Variable types

Numeric11
Text2
Categorical1
Boolean1

Alerts

department is highly overall correlated with department_idHigh correlation
department_id is highly overall correlated with departmentHigh correlation
tip_id is highly overall correlated with user_idHigh correlation
user_id is highly overall correlated with tip_idHigh correlation
days_since_prior_order has 1037769 (6.1%) missing values Missing
order_dow has 3277120 (19.3%) zeros Zeros
days_since_prior_order has 235000 (1.4%) zeros Zeros

Reproduction

Analysis started2024-11-06 21:21:03.165629
Analysis finished2024-11-06 21:26:21.625176
Duration5 minutes and 18.46 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

order_id
Real number (ℝ)

Distinct1673021
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1710930.9
Minimum1
Maximum3421081
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size129.3 MiB
2024-11-06T22:26:21.700519image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile170728.75
Q1854784
median1711184
Q32565863
95-th percentile3249789
Maximum3421081
Range3421080
Interquartile range (IQR)1711079

Descriptive statistics

Standard deviation987705.51
Coefficient of variation (CV)0.57729129
Kurtosis-1.2003968
Mean1710930.9
Median Absolute Deviation (MAD)855542
Skewness-0.00083947472
Sum2.8986995 × 1013
Variance9.7556217 × 1011
MonotonicityNot monotonic
2024-11-06T22:26:21.780488image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2970392 121
 
< 0.1%
171934 104
 
< 0.1%
1867980 102
 
< 0.1%
1384519 102
 
< 0.1%
653887 102
 
< 0.1%
3052353 101
 
< 0.1%
3048680 100
 
< 0.1%
2716231 99
 
< 0.1%
1031566 95
 
< 0.1%
1730767 95
 
< 0.1%
Other values (1673011) 16941215
> 99.9%
ValueCountFrequency (%)
1 8
 
< 0.1%
2 9
 
< 0.1%
4 13
< 0.1%
5 26
< 0.1%
8 1
 
< 0.1%
10 15
< 0.1%
13 13
< 0.1%
15 5
 
< 0.1%
18 28
< 0.1%
19 3
 
< 0.1%
ValueCountFrequency (%)
3421081 7
 
< 0.1%
3421080 9
< 0.1%
3421079 1
 
< 0.1%
3421077 4
 
< 0.1%
3421073 2
 
< 0.1%
3421071 5
 
< 0.1%
3421067 1
 
< 0.1%
3421066 6
 
< 0.1%
3421064 3
 
< 0.1%
3421061 22
< 0.1%

user_id
Real number (ℝ)

High correlation 

Distinct103104
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102720.62
Minimum1
Maximum206209
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size129.3 MiB
2024-11-06T22:26:21.857948image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10396
Q151127
median102283
Q3154005
95-th percentile195960
Maximum206209
Range206208
Interquartile range (IQR)102878

Descriptive statistics

Standard deviation59447.466
Coefficient of variation (CV)0.57872965
Kurtosis-1.1953846
Mean102720.62
Median Absolute Deviation (MAD)51474
Skewness0.011557939
Sum1.7403169 × 1012
Variance3.5340012 × 109
MonotonicityIncreasing
2024-11-06T22:26:21.935102image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
137629 2931
 
< 0.1%
182401 2929
 
< 0.1%
33731 2912
 
< 0.1%
108187 2760
 
< 0.1%
79106 2631
 
< 0.1%
5360 2602
 
< 0.1%
17738 2596
 
< 0.1%
13701 2579
 
< 0.1%
72136 2536
 
< 0.1%
181991 2535
 
< 0.1%
Other values (103094) 16915225
99.8%
ValueCountFrequency (%)
1 70
 
< 0.1%
3 88
 
< 0.1%
5 46
 
< 0.1%
6 14
 
< 0.1%
7 215
< 0.1%
8 67
 
< 0.1%
10 147
< 0.1%
11 94
< 0.1%
14 221
< 0.1%
18 50
 
< 0.1%
ValueCountFrequency (%)
206209 137
 
< 0.1%
206208 677
< 0.1%
206207 223
 
< 0.1%
206206 285
< 0.1%
206201 404
< 0.1%
206199 349
< 0.1%
206198 54
 
< 0.1%
206197 181
 
< 0.1%
206196 105
 
< 0.1%
206195 73
 
< 0.1%

order_number
Real number (ℝ)

Distinct100
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.170166
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size129.3 MiB
2024-11-06T22:26:22.010506image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median11
Q324
95-th percentile54
Maximum100
Range99
Interquartile range (IQR)19

Descriptive statistics

Standard deviation17.51389
Coefficient of variation (CV)1.0200187
Kurtosis3.3432572
Mean17.170166
Median Absolute Deviation (MAD)8
Skewness1.7731862
Sum2.90901 × 108
Variance306.73634
MonotonicityNot monotonic
2024-11-06T22:26:22.091605image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1037769
 
6.1%
2 1026053
 
6.1%
3 1025220
 
6.1%
4 982271
 
5.8%
5 874193
 
5.2%
6 787618
 
4.6%
7 713043
 
4.2%
8 647741
 
3.8%
9 595752
 
3.5%
10 545087
 
3.2%
Other values (90) 8707489
51.4%
ValueCountFrequency (%)
1 1037769
6.1%
2 1026053
6.1%
3 1025220
6.1%
4 982271
5.8%
5 874193
5.2%
6 787618
4.6%
7 713043
4.2%
8 647741
3.8%
9 595752
3.5%
10 545087
3.2%
ValueCountFrequency (%)
100 3762
< 0.1%
99 6392
< 0.1%
98 6599
< 0.1%
97 6947
< 0.1%
96 7139
< 0.1%
95 7380
< 0.1%
94 7831
< 0.1%
93 7936
< 0.1%
92 8386
< 0.1%
91 8832
0.1%

order_dow
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7410518
Minimum0
Maximum6
Zeros3277120
Zeros (%)19.3%
Negative0
Negative (%)0.0%
Memory size129.3 MiB
2024-11-06T22:26:22.151422image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0953411
Coefficient of variation (CV)0.7644296
Kurtosis-1.3396147
Mean2.7410518
Median Absolute Deviation (MAD)2
Skewness0.1768335
Sum46439546
Variance4.3904544
MonotonicityNot monotonic
2024-11-06T22:26:22.208029image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 3277120
19.3%
1 2929808
17.3%
6 2369792
14.0%
5 2204014
13.0%
2 2188126
12.9%
3 1998840
11.8%
4 1974536
11.7%
ValueCountFrequency (%)
0 3277120
19.3%
1 2929808
17.3%
2 2188126
12.9%
3 1998840
11.8%
4 1974536
11.7%
5 2204014
13.0%
6 2369792
14.0%
ValueCountFrequency (%)
6 2369792
14.0%
5 2204014
13.0%
4 1974536
11.7%
3 1998840
11.8%
2 2188126
12.9%
1 2929808
17.3%
0 3277120
19.3%

order_hour_of_day
Real number (ℝ)

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.424394
Minimum0
Maximum23
Zeros116277
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size129.3 MiB
2024-11-06T22:26:22.274297image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q110
median13
Q316
95-th percentile21
Maximum23
Range23
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.2530374
Coefficient of variation (CV)0.31681411
Kurtosis0.00043555841
Mean13.424394
Median Absolute Deviation (MAD)3
Skewness-0.049931377
Sum2.2743925 × 108
Variance18.088327
MonotonicityNot monotonic
2024-11-06T22:26:22.347560image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
10 1437440
 
8.5%
11 1429688
 
8.4%
14 1414872
 
8.4%
13 1396139
 
8.2%
15 1387447
 
8.2%
12 1371139
 
8.1%
16 1328987
 
7.8%
9 1271382
 
7.5%
17 1088769
 
6.4%
8 899191
 
5.3%
Other values (14) 3917182
23.1%
ValueCountFrequency (%)
0 116277
 
0.7%
1 63261
 
0.4%
2 36604
 
0.2%
3 27566
 
0.2%
4 28132
 
0.2%
5 46474
 
0.3%
6 151987
 
0.9%
7 467268
 
2.8%
8 899191
5.3%
9 1271382
7.5%
ValueCountFrequency (%)
23 210431
 
1.2%
22 334958
 
2.0%
21 419307
 
2.5%
20 511689
 
3.0%
19 652876
3.9%
18 850352
5.0%
17 1088769
6.4%
16 1328987
7.8%
15 1387447
8.2%
14 1414872
8.4%

days_since_prior_order
Real number (ℝ)

Missing  Zeros 

Distinct31
Distinct (%)< 0.1%
Missing1037769
Missing (%)6.1%
Infinite0
Infinite (%)0.0%
Mean11.343335
Minimum0
Maximum30
Zeros235000
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size129.3 MiB
2024-11-06T22:26:22.414315image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q15
median8
Q315
95-th percentile30
Maximum30
Range30
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.9265116
Coefficient of variation (CV)0.78693893
Kurtosis-0.20772786
Mean11.343335
Median Absolute Deviation (MAD)4
Skewness1.0076553
Sum1.8040969 × 108
Variance79.68261
MonotonicityNot monotonic
2024-11-06T22:26:22.482424image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
7 1807177
 
10.7%
30 1732380
 
10.2%
6 1294189
 
7.6%
5 1095107
 
6.5%
4 1069148
 
6.3%
8 1000535
 
5.9%
3 957148
 
5.6%
2 748607
 
4.4%
9 634130
 
3.7%
14 540232
 
3.2%
Other values (21) 5025814
29.7%
(Missing) 1037769
 
6.1%
ValueCountFrequency (%)
0 235000
 
1.4%
1 482217
 
2.8%
2 748607
4.4%
3 957148
5.6%
4 1069148
6.3%
5 1095107
6.5%
6 1294189
7.6%
7 1807177
10.7%
8 1000535
5.9%
9 634130
 
3.7%
ValueCountFrequency (%)
30 1732380
10.2%
29 94961
 
0.6%
28 136014
 
0.8%
27 108168
 
0.6%
26 95503
 
0.6%
25 96798
 
0.6%
24 104105
 
0.6%
23 121144
 
0.7%
22 165559
 
1.0%
21 236470
 
1.4%

product_id
Real number (ℝ)

Distinct49258
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25577.236
Minimum1
Maximum49688
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size129.3 MiB
2024-11-06T22:26:22.557812image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3376
Q113521
median25256
Q337947
95-th percentile47570
Maximum49688
Range49687
Interquartile range (IQR)24426

Descriptive statistics

Standard deviation14100.859
Coefficient of variation (CV)0.55130504
Kurtosis-1.1420139
Mean25577.236
Median Absolute Deviation (MAD)12080
Skewness-0.02087385
Sum4.3333556 × 1011
Variance1.9883422 × 108
MonotonicityNot monotonic
2024-11-06T22:26:22.640564image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24852 244850
 
1.4%
13176 197298
 
1.2%
21137 138445
 
0.8%
21903 126674
 
0.7%
47209 111509
 
0.7%
47766 91565
 
0.5%
47626 80748
 
0.5%
16797 74606
 
0.4%
26209 74107
 
0.4%
27966 71330
 
0.4%
Other values (49248) 15731104
92.9%
ValueCountFrequency (%)
1 1021
< 0.1%
2 33
 
< 0.1%
3 130
 
< 0.1%
4 164
 
< 0.1%
5 4
 
< 0.1%
6 3
 
< 0.1%
7 23
 
< 0.1%
8 66
 
< 0.1%
9 79
 
< 0.1%
10 1282
< 0.1%
ValueCountFrequency (%)
49688 47
 
< 0.1%
49687 6
 
< 0.1%
49686 85
 
< 0.1%
49685 28
 
< 0.1%
49684 4
 
< 0.1%
49683 50205
0.3%
49682 60
 
< 0.1%
49681 28
 
< 0.1%
49680 501
 
< 0.1%
49679 60
 
< 0.1%

add_to_cart_order
Real number (ℝ)

Distinct121
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.3960477
Minimum1
Maximum121
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size129.3 MiB
2024-11-06T22:26:22.715092image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q312
95-th percentile22
Maximum121
Range120
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.1715785
Coefficient of variation (CV)0.85416123
Kurtosis5.1324417
Mean8.3960477
Median Absolute Deviation (MAD)4
Skewness1.7895549
Sum1.4224782 × 108
Variance51.431538
MonotonicityNot monotonic
2024-11-06T22:26:22.792800image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1673021
 
9.9%
2 1591820
 
9.4%
3 1494855
 
8.8%
4 1387036
 
8.2%
5 1271349
 
7.5%
6 1152561
 
6.8%
7 1034543
 
6.1%
8 920714
 
5.4%
9 815300
 
4.8%
10 719304
 
4.2%
Other values (111) 4881733
28.8%
ValueCountFrequency (%)
1 1673021
9.9%
2 1591820
9.4%
3 1494855
8.8%
4 1387036
8.2%
5 1271349
7.5%
6 1152561
6.8%
7 1034543
6.1%
8 920714
5.4%
9 815300
4.8%
10 719304
4.2%
ValueCountFrequency (%)
121 1
< 0.1%
120 1
< 0.1%
119 1
< 0.1%
118 1
< 0.1%
117 1
< 0.1%
116 1
< 0.1%
115 1
< 0.1%
114 1
< 0.1%
113 1
< 0.1%
112 1
< 0.1%
Distinct49258
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size1.2 GiB
2024-11-06T22:26:23.028584image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length159
Median length124
Mean length25.020709
Min length3

Characters and Unicode

Total characters423906752
Distinct characters112
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1230 ?
Unique (%)< 0.1%

Sample

1st rowSoda
2nd rowOrganic Unsweetened Vanilla Almond Milk
3rd rowOriginal Beef Jerky
4th rowAged White Cheddar Popcorn
5th rowXL Pick-A-Size Paper Towel Rolls
ValueCountFrequency (%)
organic 5328597
 
8.3%
1050303
 
1.6%
milk 905101
 
1.4%
cheese 764222
 
1.2%
yogurt 702792
 
1.1%
whole 636286
 
1.0%
free 604825
 
0.9%
original 520726
 
0.8%
water 517317
 
0.8%
baby 516782
 
0.8%
Other values (11959) 52990510
82.1%
2024-11-06T22:26:23.366714image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
47687292
 
11.2%
e 39810195
 
9.4%
a 36057670
 
8.5%
r 29922629
 
7.1%
i 25007978
 
5.9%
n 23154655
 
5.5%
o 18643600
 
4.4%
l 17536874
 
4.1%
t 17497147
 
4.1%
s 14170037
 
3.3%
Other values (102) 154418675
36.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 423906752
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
47687292
 
11.2%
e 39810195
 
9.4%
a 36057670
 
8.5%
r 29922629
 
7.1%
i 25007978
 
5.9%
n 23154655
 
5.5%
o 18643600
 
4.4%
l 17536874
 
4.1%
t 17497147
 
4.1%
s 14170037
 
3.3%
Other values (102) 154418675
36.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 423906752
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
47687292
 
11.2%
e 39810195
 
9.4%
a 36057670
 
8.5%
r 29922629
 
7.1%
i 25007978
 
5.9%
n 23154655
 
5.5%
o 18643600
 
4.4%
l 17536874
 
4.1%
t 17497147
 
4.1%
s 14170037
 
3.3%
Other values (102) 154418675
36.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 423906752
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
47687292
 
11.2%
e 39810195
 
9.4%
a 36057670
 
8.5%
r 29922629
 
7.1%
i 25007978
 
5.9%
n 23154655
 
5.5%
o 18643600
 
4.4%
l 17536874
 
4.1%
t 17497147
 
4.1%
s 14170037
 
3.3%
Other values (102) 154418675
36.4%

aisle_id
Real number (ℝ)

Distinct134
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.174274
Minimum1
Maximum134
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size129.3 MiB
2024-11-06T22:26:23.456813image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile16
Q131
median83
Q3107
95-th percentile123
Maximum134
Range133
Interquartile range (IQR)76

Descriptive statistics

Standard deviation38.197941
Coefficient of variation (CV)0.53668185
Kurtosis-1.3244584
Mean71.174274
Median Absolute Deviation (MAD)33
Skewness-0.16629414
Sum1.2058514 × 109
Variance1459.0827
MonotonicityNot monotonic
2024-11-06T22:26:23.543102image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24 1895765
 
11.2%
83 1792105
 
10.6%
123 922576
 
5.4%
120 753242
 
4.4%
21 510381
 
3.0%
84 459496
 
2.7%
115 439984
 
2.6%
107 376282
 
2.2%
91 331730
 
2.0%
112 304667
 
1.8%
Other values (124) 9156008
54.0%
ValueCountFrequency (%)
1 37794
 
0.2%
2 43208
 
0.3%
3 239790
1.4%
4 104544
0.6%
5 32540
 
0.2%
6 19342
 
0.1%
7 17508
 
0.1%
8 18956
 
0.1%
9 114889
0.7%
10 4860
 
< 0.1%
ValueCountFrequency (%)
134 6032
 
< 0.1%
133 9715
 
0.1%
132 3125
 
< 0.1%
131 139180
0.8%
130 82028
0.5%
129 101521
0.6%
128 100788
0.6%
127 21506
 
0.1%
126 10370
 
0.1%
125 18537
 
0.1%

department_id
Real number (ℝ)

High correlation 

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9161701
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size129.3 MiB
2024-11-06T22:26:23.614896image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median9
Q316
95-th percentile19
Maximum21
Range20
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.2813163
Coefficient of variation (CV)0.63344176
Kurtosis-1.5597187
Mean9.9161701
Median Absolute Deviation (MAD)5
Skewness0.15272867
Sum1.6800209 × 108
Variance39.454934
MonotonicityNot monotonic
2024-11-06T22:26:23.684683image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
4 4957175
29.3%
16 2816030
16.6%
19 1509667
 
8.9%
7 1402323
 
8.3%
1 1170018
 
6.9%
13 984550
 
5.8%
3 613344
 
3.6%
15 560188
 
3.3%
20 546160
 
3.2%
9 452992
 
2.7%
Other values (11) 1929789
 
11.4%
ValueCountFrequency (%)
1 1170018
 
6.9%
2 19342
 
0.1%
3 613344
 
3.6%
4 4957175
29.3%
5 82145
 
0.5%
6 141130
 
0.8%
7 1402323
 
8.3%
8 51742
 
0.3%
9 452992
 
2.7%
10 18036
 
0.1%
ValueCountFrequency (%)
21 39026
 
0.2%
20 546160
 
3.2%
19 1509667
8.9%
18 218996
 
1.3%
17 386709
 
2.3%
16 2816030
16.6%
15 560188
 
3.3%
14 370020
 
2.2%
13 984550
 
5.8%
12 369467
 
2.2%

aisle
Text

Distinct134
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 GiB
2024-11-06T22:26:23.895641image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length29
Median length23
Mean length14.45503
Min length3

Characters and Unicode

Total characters244900527
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsoft drinks
2nd rowsoy lactosefree
3rd rowpopcorn jerky
4th rowpopcorn jerky
5th rowpaper goods
ValueCountFrequency (%)
fresh 4089173
 
11.4%
vegetables 2879334
 
8.0%
fruits 2827296
 
7.9%
packaged 1672328
 
4.6%
frozen 908224
 
2.5%
water 879968
 
2.4%
yogurt 753242
 
2.1%
ice 529363
 
1.5%
cheese 510381
 
1.4%
milk 459496
 
1.3%
Other values (194) 20505966
56.9%
2024-11-06T22:26:24.187254image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 35002107
14.3%
s 23210298
 
9.5%
r 20757851
 
8.5%
a 19212352
 
7.8%
19072535
 
7.8%
t 14378351
 
5.9%
f 10534058
 
4.3%
i 9796446
 
4.0%
o 9247781
 
3.8%
g 9154511
 
3.7%
Other values (16) 74534237
30.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 244900527
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 35002107
14.3%
s 23210298
 
9.5%
r 20757851
 
8.5%
a 19212352
 
7.8%
19072535
 
7.8%
t 14378351
 
5.9%
f 10534058
 
4.3%
i 9796446
 
4.0%
o 9247781
 
3.8%
g 9154511
 
3.7%
Other values (16) 74534237
30.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 244900527
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 35002107
14.3%
s 23210298
 
9.5%
r 20757851
 
8.5%
a 19212352
 
7.8%
19072535
 
7.8%
t 14378351
 
5.9%
f 10534058
 
4.3%
i 9796446
 
4.0%
o 9247781
 
3.8%
g 9154511
 
3.7%
Other values (16) 74534237
30.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 244900527
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 35002107
14.3%
s 23210298
 
9.5%
r 20757851
 
8.5%
a 19212352
 
7.8%
19072535
 
7.8%
t 14378351
 
5.9%
f 10534058
 
4.3%
i 9796446
 
4.0%
o 9247781
 
3.8%
g 9154511
 
3.7%
Other values (16) 74534237
30.4%

department
Categorical

High correlation 

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size920.9 MiB
produce
4957175 
dairy eggs
2816030 
snacks
1509667 
beverages
1402323 
frozen
1170018 
Other values (16)
5087023 

Length

Max length15
Median length13
Mean length7.997576
Min length4

Characters and Unicode

Total characters135496820
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowbeverages
2nd rowdairy eggs
3rd rowsnacks
4th rowsnacks
5th rowhousehold

Common Values

ValueCountFrequency (%)
produce 4957175
29.3%
dairy eggs 2816030
16.6%
snacks 1509667
 
8.9%
beverages 1402323
 
8.3%
frozen 1170018
 
6.9%
pantry 984550
 
5.8%
bakery 613344
 
3.6%
canned goods 560188
 
3.3%
deli 546160
 
3.2%
dry goods pasta 452992
 
2.7%
Other values (11) 1929789
 
11.4%

Length

2024-11-06T22:26:24.275744image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
produce 4957175
22.7%
dairy 2816030
12.9%
eggs 2816030
12.9%
snacks 1509667
 
6.9%
beverages 1402323
 
6.4%
frozen 1170018
 
5.4%
goods 1013180
 
4.6%
pantry 984550
 
4.5%
bakery 613344
 
2.8%
canned 560188
 
2.6%
Other values (16) 3984576
18.3%

Most occurring characters

ValueCountFrequency (%)
e 17263109
12.7%
r 13393276
 
9.9%
a 11320813
 
8.4%
d 11101901
 
8.2%
s 10412021
 
7.7%
o 10223843
 
7.5%
g 8086589
 
6.0%
c 7342351
 
5.4%
p 6679635
 
4.9%
n 5480203
 
4.0%
Other values (13) 34193079
25.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 135496820
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 17263109
12.7%
r 13393276
 
9.9%
a 11320813
 
8.4%
d 11101901
 
8.2%
s 10412021
 
7.7%
o 10223843
 
7.5%
g 8086589
 
6.0%
c 7342351
 
5.4%
p 6679635
 
4.9%
n 5480203
 
4.0%
Other values (13) 34193079
25.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 135496820
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 17263109
12.7%
r 13393276
 
9.9%
a 11320813
 
8.4%
d 11101901
 
8.2%
s 10412021
 
7.7%
o 10223843
 
7.5%
g 8086589
 
6.0%
c 7342351
 
5.4%
p 6679635
 
4.9%
n 5480203
 
4.0%
Other values (13) 34193079
25.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 135496820
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 17263109
12.7%
r 13393276
 
9.9%
a 11320813
 
8.4%
d 11101901
 
8.2%
s 10412021
 
7.7%
o 10223843
 
7.5%
g 8086589
 
6.0%
c 7342351
 
5.4%
p 6679635
 
4.9%
n 5480203
 
4.0%
Other values (13) 34193079
25.2%

tip_id
Real number (ℝ)

High correlation 

Distinct1673021
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1706272.9
Minimum0
Maximum3421082
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size129.3 MiB
2024-11-06T22:26:24.347654image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile172187
Q1850523
median1703692
Q32559312
95-th percentile3251135
Maximum3421082
Range3421082
Interquartile range (IQR)1708789

Descriptive statistics

Standard deviation986164.09
Coefficient of variation (CV)0.57796388
Kurtosis-1.1956622
Mean1706272.9
Median Absolute Deviation (MAD)854279
Skewness0.0052109728
Sum2.8908078 × 1013
Variance9.7251962 × 1011
MonotonicityIncreasing
2024-11-06T22:26:24.628111image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1327221 121
 
< 0.1%
1204060 104
 
< 0.1%
519395 102
 
< 0.1%
2115275 102
 
< 0.1%
1397638 102
 
< 0.1%
568301 101
 
< 0.1%
1801232 100
 
< 0.1%
2717969 99
 
< 0.1%
471303 95
 
< 0.1%
59030 95
 
< 0.1%
Other values (1673011) 16941215
> 99.9%
ValueCountFrequency (%)
0 5
< 0.1%
1 6
< 0.1%
2 5
< 0.1%
3 5
< 0.1%
4 8
< 0.1%
5 4
< 0.1%
6 5
< 0.1%
7 6
< 0.1%
8 6
< 0.1%
9 9
< 0.1%
ValueCountFrequency (%)
3421082 8
 
< 0.1%
3421081 9
< 0.1%
3421080 20
< 0.1%
3421079 8
 
< 0.1%
3421078 9
< 0.1%
3421077 3
 
< 0.1%
3421076 12
< 0.1%
3421075 10
< 0.1%
3421074 2
 
< 0.1%
3421073 3
 
< 0.1%

tip
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.2 MiB
False
9523085 
True
7419151 
ValueCountFrequency (%)
False 9523085
56.2%
True 7419151
43.8%
2024-11-06T22:26:24.705438image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Interactions

2024-11-06T22:25:44.176771image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:23:56.375454image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:24:06.875708image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:24:17.196442image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:24:27.574285image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:24:38.075121image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:24:48.651343image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:24:59.920761image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:25:11.176562image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:25:21.854559image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:25:33.115125image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:25:45.175991image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:23:57.315462image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:24:07.754264image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:24:18.109465image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:24:28.513292image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:24:39.010966image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:24:49.675740image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:25:00.936716image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:25:12.128710image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:25:22.871747image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:25:34.110198image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:25:46.179286image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:23:58.246648image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:24:08.677810image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:24:18.999215image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:24:29.441258image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:24:39.940907image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:24:50.696862image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:25:01.956606image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:25:13.077578image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:25:23.882349image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:25:35.090860image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:25:47.294109image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:23:59.181329image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:24:09.613124image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:24:19.917168image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:24:30.344793image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:24:40.868710image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:24:51.716568image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:25:02.972848image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:25:14.022645image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:25:24.896750image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:25:36.079201image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:25:48.288375image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:24:00.107409image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:24:10.536000image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:24:20.851147image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:24:31.274442image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:24:41.769060image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:24:52.752979image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:25:03.983681image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:25:14.969311image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:25:25.915514image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:25:37.057670image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:25:49.299508image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:24:01.121308image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:24:11.540941image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:24:21.865521image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:24:32.284224image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:24:42.772151image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:24:53.704331image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:25:05.065677image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:25:16.002700image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:25:27.002022image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:25:38.124257image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:25:50.328811image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:24:02.073097image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:24:12.472478image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:24:22.808075image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:24:33.214548image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:24:43.723276image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:24:54.721577image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:25:06.059309image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:25:16.956892image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:25:28.026638image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:25:39.125222image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:25:51.339413image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:24:03.032435image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:24:13.400811image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:24:23.745427image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:24:34.144171image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:24:44.674776image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:24:55.756628image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:25:07.076849image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:25:17.882724image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:25:29.041458image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:25:40.122159image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:25:52.357630image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:24:03.986220image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:24:14.337020image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:24:24.695519image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:24:35.072729image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:24:45.624116image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:24:56.789364image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:25:08.092280image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:25:18.836580image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:25:30.028501image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:25:41.138894image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:25:53.375835image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:24:04.936577image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:24:15.277805image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:24:25.641496image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:24:35.997363image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:24:46.584322image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:24:57.834943image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:25:09.118663image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:25:19.800885image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:25:31.036504image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:25:42.109615image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:25:54.276396image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:24:05.950734image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:24:16.268897image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:24:26.640690image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:24:37.136717image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:24:47.595209image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:24:58.892690image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:25:10.222327image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:25:20.833421image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:25:32.127616image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-06T22:25:43.167752image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-11-06T22:26:24.754042image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
add_to_cart_orderaisle_iddays_since_prior_orderdepartmentdepartment_idorder_doworder_hour_of_dayorder_idorder_numberproduct_idtiptip_iduser_id
add_to_cart_order1.0000.0060.0750.0340.016-0.015-0.013-0.0010.0030.0080.0200.0030.003
aisle_id0.0061.0000.0060.4380.021-0.002-0.0020.0000.0010.0050.0600.0010.001
days_since_prior_order0.0750.0061.0000.018-0.001-0.044-0.002-0.000-0.3830.0010.1790.0060.006
department0.0340.4380.0181.0001.0000.0240.0150.0010.0170.0750.1080.0040.004
department_id0.0160.021-0.0011.0001.0000.006-0.011-0.0000.005-0.0220.0880.0010.001
order_dow-0.015-0.002-0.0440.0240.0061.0000.0120.0000.015-0.0030.143-0.003-0.003
order_hour_of_day-0.013-0.002-0.0020.015-0.0110.0121.0000.000-0.0480.0010.097-0.000-0.000
order_id-0.0010.000-0.0000.001-0.0000.0000.0001.000-0.000-0.0000.002-0.001-0.001
order_number0.0030.001-0.3830.0170.0050.015-0.048-0.0001.000-0.0010.142-0.004-0.004
product_id0.0080.0050.0010.075-0.022-0.0030.001-0.000-0.0011.0000.017-0.000-0.000
tip0.0200.0600.1790.1080.0880.1430.0970.0020.1420.0171.0000.0090.010
tip_id0.0030.0010.0060.0040.001-0.003-0.000-0.001-0.004-0.0000.0091.0001.000
user_id0.0030.0010.0060.0040.001-0.003-0.000-0.001-0.004-0.0000.0101.0001.000

Missing values

2024-11-06T22:25:55.417125image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-06T22:26:01.661565image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

order_iduser_idorder_numberorder_doworder_hour_of_daydays_since_prior_orderproduct_idadd_to_cart_orderproduct_nameaisle_iddepartment_idaisledepartmenttip_idtip
025393291128NaN1961Soda777soft drinksbeverages0False
125393291128NaN140842Organic Unsweetened Vanilla Almond Milk9116soy lactosefreedairy eggs0False
225393291128NaN124273Original Beef Jerky2319popcorn jerkysnacks0False
325393291128NaN260884Aged White Cheddar Popcorn2319popcorn jerkysnacks0False
425393291128NaN264055XL Pick-A-Size Paper Towel Rolls5417paper goodshousehold0False
52398795123715.01961Soda777soft drinksbeverages1False
62398795123715.0102582Pistachios11719nuts seeds dried fruitsnacks1False
72398795123715.0124273Original Beef Jerky2319popcorn jerkysnacks1False
82398795123715.0131764Bag of Organic Bananas244fresh fruitsproduce1False
92398795123715.0260885Aged White Cheddar Popcorn2319popcorn jerkysnacks1False
order_iduser_idorder_numberorder_doworder_hour_of_daydays_since_prior_orderproduct_idadd_to_cart_orderproduct_nameaisle_iddepartment_idaisledepartmenttip_idtip
169422262977660206209131127.065678Chocolate Peanut Butter Protein Bar319energy granola barssnacks3421081False
169422272977660206209131127.0229209Roasted & Salted Shelled Pistachios11719nuts seeds dried fruitsnacks3421081False
169422282722312062091461430.068461Diet Pepsi Pack777soft drinksbeverages3421082False
169422292722312062091461430.094052Calcium Enriched 100% Lactose Free Fat Free Milk9116soy lactosefreedairy eggs3421082False
169422302722312062091461430.0248523Banana244fresh fruitsproduce3421082False
169422312722312062091461430.0406034Fabric Softener Sheets7517laundryhousehold3421082False
169422322722312062091461430.0156555Dark Chocolate Mint Snacking Chocolate4519candy chocolatesnacks3421082False
169422332722312062091461430.0426066Phish Food Frozen Yogurt371ice cream icefrozen3421082False
169422342722312062091461430.0379667French Baguette Bread1123breadbakery3421082False
169422352722312062091461430.0392168Original Multigrain Spoonfuls Cereal12114cerealbreakfast3421082False